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Finding the best set of K paths through a trellis with application to multitarget tracking
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1109/tac.1977.1101500openalex-snapshot
Application of 0-1 integer programming to multitarget tracking problems
inferredcites →https://doi.org/10.1109/tac.1978.1101790openalex-snapshot
Tracking methods in a multitarget environment
inferredcites →https://doi.org/10.1109/tac.1979.1102177openalex-snapshot
An algorithm for tracking multiple targets
inferredcites →https://doi.org/10.1109/tac.1984.1103466openalex-snapshot
Application of state estimation to target tracking
inferredcites →https://doi.org/10.1109/tac.1984.1103597openalex-snapshot
Joint probabilistic data association for multitarget tracking with possibly unresolved measurements and maneuvers
inferredcites →https://doi.org/10.1109/tac.1986.1104306openalex-snapshot
Tracking and classifying multiple targets without a priori identification
inferredcites →https://doi.org/10.1109/taes.1985.310548openalex-snapshot
Dynamic Programming Solution for Detecting Dim Moving Targets
inferredcites →https://doi.org/10.1109/tcom.1971.1090700openalex-snapshot
Convolutional Codes and Their Performance in Communication Systems
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Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
inferredcites →https://doi.org/10.1109/tit.1974.1055256openalex-snapshot
Derivation and evaluation of improved tracking filter for use in dense multitarget environments
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Efficient algorithms for finding the K best paths through a trellis
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Application of the EM algorithm for the multitarget/multisensor tracking problem
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Graph Convolution Neural Network-Based Data Association for Online Multi-Object Tracking
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